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Bayesian State-space Implementation of Schaefer Production Model for Assessment of Stock Status for Multi-gear Fishery

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Relation http://eprints.cmfri.org.in/14401/
http://www.isas.org.in/jisas
 
Title Bayesian State-space Implementation of Schaefer Production Model for
Assessment of Stock Status for Multi-gear Fishery
 
Creator Varghese, Eldho
Sathianandan, T V
Jayasankar, J
Kuriakose, Somy
Mini, K G
Muktha, M
 
Subject Statistical Designs
Analytical models
Marine Fisheries
 
Description Knowing the status of marine fish stock is of utmost importance to develop management strategies for sustainable harvest of marine fishery resources.
A widely accepted approach towards this is to derive sustainable harvest levels using time series data on fish catch and fishing effort based on fish
stock assessment models like Schaefer’s model that describe the biomass dynamics. In India, the marine fishery is of complex multi-species nature
where in different species are caught by a number of fishing gears and each gear harvests a number of species making it difficult to obtain the fishing
effort corresponding to each fish species. Since the capacity of the gears varies, the effort made to catch a resource cannot be considered as the sum
of efforts expended by different fishing gears. Hence, it demands the importance of effort standardisation for making use in stock assessment models.
This paper describes a methodology for the standardization of fishing efforts and assessing fish stock status using Bayesian state-space implementation
of the Schaefer production model (BSM). A Monte Carlo based method namely Catch-Maximum Sustainable Yield (CMSY), has also been used for
estimating fisheries reference points from landings and a proxy for biomass using resilience of the species. The procedure has been illustrated with
data on Indian mackerel (Rastrelliger Kanagurta) collected from the coastal state of Andhra Pradesh, India during 1997-2018. Maximum Sustainable
Yield (MSY) of Indian mackerel for Andhra Pradesh has been estimated. A comparison between both CMSY and BSM methods have been made and
found that the estimates are in close agreements.
 
Publisher Indian Society of Agricultural Statistics
 
Date 2020
 
Type Article
PeerReviewed
 
Format text
 
Language en
 
Identifier http://eprints.cmfri.org.in/14401/1/JISAS_2020_Eldho%20Varghese_Bayesian%20State-space%20Implementation%20of%20Schaefer%20Production%20Model.pdf
Varghese, Eldho and Sathianandan, T V and Jayasankar, J and Kuriakose, Somy and Mini, K G and Muktha, M (2020) Bayesian State-space Implementation of Schaefer Production Model for Assessment of Stock Status for Multi-gear Fishery. Journal of the Indian Society of Agricultural Statistics, 74 (1). pp. 33-40.